26 to 39 of 39 Retrieval-Augmented Generation Jobs in Central London

quality controls. Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC). Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures. Familiarity with LLMOps tooling (LangSmith, LangChain, LangGraph). Understanding of model evaluation, validation, and production monitoring. Strong problem-solving skills and ability to communicate … security clearance; no sponsorship provided. Desirable: Advanced LLM techniques: agents, tool use, and agentic workflows. Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications. Experience with feature stores (Feast, AWS Feature Store). Containerisation (Docker) and orchestration (Kubernetes, ECS). Infrastructure as Code (Terraform, CloudFormation). Large-scale data ...

What We're Looking For Must-Haves: 4+ years experience writing production ready code for machine learning systems 2+ years experience developing conversational AI, RAG, agentic systems or LLM-based products Familiarity with RAG orchestration frameworks such as LangChain or LlamaIndex.2+ Experience with production optimisation for RAG systems, including latency ...

applications providing cutting-edge scientific tooling to scientists Shape the AI strategy and technology stack Develop AI features leveraging various methodologies (e.g., LLMs, RAG, traditional ML) Design and implement data strategies for AI development and training Requirements 4+ years' experience across Fullstack development (Python/Modern JavaScript (React ...

identify AI integration opportunities for productivity, quality, or innovation gains. Design, code, train, and test AI models tailored to use cases (e.g., NLP, CV, RAG, forecasting, etc.). Perform effort estimation and model validation and monitor deployment success and cost-performance trade-offs. Collaborate closely with local technical leads while … Learning , Deep Learning , Natural Language Processing , Computer Vision , Generative AI , Reinforcement Learning , LLMs (e.g., GPT, Claude, Mistral, LLaMA, DeepSeek, etc.). Agent-based architectures , RAG , prompt engineering , chatbots , classification , summarization , speech-to-text , image understanding. Cloud platforms (AWS, Azure, GCP). ML Ops tools and deployment workflows. AI-assisted development ...

powered Predictive Intelligence Platform for corporates and financial institutions. Their technology connects market events, client data, and emerging opportunities through advanced agentic AI and RAG-based systems, helping financial institutions anticipate client needs before they arise. They have built a live MVP in record time, with enterprise client demos underway … lead the design and productionisation of the company’s core AI systems. You will architect, implement, evaluate, and scale RAG pipelines, agentic workflows, and LLM-based intelligence frameworks, driving real-world impact across financial markets. The role is approximately 80% hands-on and 20% strategic, covering Python engineering, system architecture ...

OpenAI environment Build Prof. T v0.1 — our AI tutor trained on curriculum, assessments, and teaching materials Create retrieval and vector-based pipelines (RAG) for structured learning content Build internal tools for academics to upload, review, and refine materials Work with leadership to define our long-term AI roadmap … entire institution. What we’re looking for Essential Experience with Azure OpenAI, OpenAI, LangChain, or similar frameworks Strong Python engineering skills Practical experience building RAG systems, embeddings, vector stores Comfort with cloud architecture (Azure preferred) Ability to move fast, prototype, and ship Experience building MVPs, internal tools, or early-stage ...

NetMind.ai, we’re building the next-generation AI/ML platform powered by a global decentralized GPU infrastructure. Our mission is to deliver the simplest and most accessible generative AI solutions on the market and democratize access to AI technology globally. Our AI services range from inference model … within days (or hours) to support client pitches. AI Stack Integration: Utilize Claude Code Agent, Google Gemini, and OpenAI APIs to implement features like RAG, document analysis, and automated workflows. Full-Stack Lite: Use Python and Streamlit to create clean, interactive, and client-ready user interfaces. Prompt Engineering: Design ...

products at scale. Sitting at the core of AI delivery, you will design, build, and operate the runtime, infrastructure, and operational layers supporting RAG pipelines, LLM orchestration, vector search, and evaluation workflows across AWS and Databricks. Working closely with senior AI engineers and product teams, you'll ensure AI systems … With further scope of responsibilities detailed below: Own and evolve the AI platform powering conversational assistants and generative AI products. Build, operate, and optimise RAG and LLM-backed services, improving latency, reliability, and cost. Design and run cloud-native AI services across AWS and Databricks, including ingestion and embedding pipelines. ...

extremely experienced Head of Backend and VP of engineering as well as closing working with a team of doctors helping build the next generation of AI powered clinical knowledge tools. If you are an experienced senior backend engineer this is one of those rare roles where your decisions … build robust, scalable cloud-first backend systems. Contribute to infrastructure-as-code practices and DevOps tooling. Build and maintain LLM-powered features such as RAG pipelines for clinical information. Develop and optimise agentic document extraction workflows to process clinical PDFs, policies, and handbooks into structured formats. Collaborate closely with ...

microservices Docker, CI/CD pipelines Cloud security & compliance (HIPAA/GDPR/NHS DTAC) Frontend React TypeScript Tailwind Component-driven development AI/RAG/Knowledge Pipelines LLM-powered features RAG pipelines (embedding models, chunking strategies, semantic search) Document extraction/processing (PDF parsing, OCR, agentic pipelines) Clinical content … code and deploy serverless services. Help architect systems that scale to hundreds of thousands of clinicians. AI & Clinical Knowledge Retrieval Build RAG workflows that safely surface medical knowledge. Develop clinical document ingestion pipelines that process PDFs, protocols, and policies. Validate AI-generated outputs with clinical context and domain ...

Founding Backend Engineer | Python, TypeScript, LLMs, RAG | Seed-Funded B2B SaaS | £100,000 - £160,000 + Early Stage Equity | London, Hybrid | Can Sponsor + Relocate The Company We’re supporting a well-funded Seed-stage B2B SaaS startup backed by top-tier European investors. They are automating a traditionally manual … expansion, deeper automation, and building intelligent systems that turn unstructured data into actionable insights at scale. About the Founding Backend Engineer | Python, TypeScript, LLMs, RAG | Seed-Funded B2B SaaS | £100,000 - £160,000 + Early Stage Equity | London, Hybrid | Can Sponsor + Relocate As the Founding AI Engineer, you will ...

build a predictive AI platform that surfaces opportunities before clients even know they exist? Have you led end-to-end delivery of LLM/RAG/agentic systems in production? Ready to become the technical owner of an intelligence engine at an early-stage startup? A high-growth AI/… technical scope and the chance to define the product from the ground up. The Lead AI Engineer will architect and productionise advanced LLM/RAG systems, design agentic workflows, own evaluations and guardrails, and integrate AI modules into a scalable enterprise-grade platform. You’ll collaborate with domain experts from ...

systems from day one — scalable architectures, APIs, and AI-powered tools. Build and deploy LLM-based applications using frameworks such as LangChain, semantic search, RAG, and fine-tuning (SFT/RL). Develop backend logic and data pipelines to power intelligent automation and real-time processing. Manage infrastructure: relational … track record of building and shipping production-ready systems. Hands-on experience with distributed systems, APIs, databases, and cloud infrastructure. Familiarity with LLMs, LangChain, RAG, or fine-tuning techniques (SFT/RL). Strong problem-solving and debugging abilities with a proactive, builder’s mindset. Comfortable in a fast-paced ...

Hugging Face Transformers/PEFT, tokenisers, spaCy or similar) Experience shipping NLP systems: prompt engineering, fine-tuning (e.g., LoRA/PEFT), vector search, and RAG-based services Great knowledge of NLP algorithms: tokenisation, embeddings, attention, language modelling, text classification/generation, and information retrieval Desirable Skills ...